Follow-up on the `run_metadata` changes

[bcdurak]

Describe changes

This PR optimizes the way that we store run metadata related to different entities. (addresses the review comment here)

In our old implementation, when someone calls log_metadata, it is possible that they attach the same metadata to different entities such as pipeline runs, step runs, and model versions. This process used to create X different entries with the same key-value pair in the metadata table.

In order to optimize this process, this PR separates the previous run metadata table into two tables:

• One that holds the actual key-value pair.
• One that links this pair to different resources.

For this to work, I also implemented a new RunMetadataRequest model, that can hold more than one entity per key-value pair. In all the previous calls that used this request model, all affected models and schemas were adjusted accordingly.

Other related changes

• Preview for the docs changes
• Starter Template PR
• E2E Batch Template PR
• NLP Template PR
• LLM Finetuning Template PR

Remaining TODOs

• Add more tests (model_version_id logging)

Pre-requisites

Please ensure you have done the following:

• [X] I have read the CONTRIBUTING.md document.
• [X] If my change requires a change to docs, I have updated the documentation accordingly.
• [X] I have added tests to cover my changes.
• [X] I have based my new branch on develop and the open PR is targeting develop. If your branch wasn't based on develop read Contribution guide on rebasing branch to develop.
• [X] If my changes require changes to the dashboard, these changes are communicated/requested.

Types of changes

• [ ] Bug fix (non-breaking change which fixes an issue)
• [X] New feature (non-breaking change which adds functionality)
• [ ] Breaking change (fix or feature that would cause existing functionality to change)
• [ ] Other (add details above)

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[bcdurak]

@htahir1 It would be great if you could also take a look at the docs changes.

[github-actions[bot]]

LLM Finetuning template updates in examples/llm_finetuning have been pushed.

[github-actions[bot]]

Classification template updates in examples/mlops_starter have been pushed.

[github-actions[bot]]

E2E template updates in examples/e2e have been pushed.

[github-actions[bot]]

NLP template updates in examples/e2e_nlp have been pushed.

[avishniakov]

@bcdurak was nice to talk to you today, here is a brief summary of potential improvements with the current metadata:

• Metadata values duplication. It is not a problem for small entries, but it becomes heavy for long metadata entries since they will be logged again and again. It can be solved by normalizing the metadata table a bit and extracting the value to a separate table. Next, you would need to think about garbage collection to remove orphaned entries periodically. Also, this makes values immutable, obviously, since they can be used somewhere else.
• Metadata logged into the same key creates a new record, not removing the old one. This one I see as critical for consistency. I suggest you either overwrite the value for the existing key or store values as listed under the same key - design choice, IMO.
• Metadata logged inside steps is not logged on rerun in cached mode. This one is quite questionable if we should do this or not, but this would be a consistent UX in comparison to artifacts, for instance.

LMK, if you want to talk more.

P.S. @htahir1 FYI, this might be relevant for product road mapping and prioritization of tasks, so your input is warmly welcomed.

[bcdurak]

@avishniakov Just had a discussion with @htahir1, here is what we decided to do:

1. Solving the metadata values duplication with the proposed structure is out of the scope of this ticket for now. We might solve this in a separate PR in the future.
2. Metadata logged into the same key creates a new record, not removing the old one. I have to discuss this with @znegrin but it is likely that we keep the same behaviour. In this case, I have to make sure that our users get the right value back, may it be a list of values or the latest value for that matter.
3. As for cached steps, this is a very valid concern. I will try to figure out a way to store if the metadata entry was created during a step execution and duplicate the same behavior.

[bcdurak]

@avishniakov I made some significant changes after our last discussion:

• Now, each time you call log_metadata, only one entity will be linked to the newly generated metadata entry.
• We now differentiate the metadata that is attached to a step during its execution with a cached flag. This helps us to create a new link if another step is using the cached execution of the original step and we link the original metadata to the new step.
• I have added some additional utility classes as you suggested (RunMetadataResource) that are used in the process of generating run metadata.
• I have added two additional methods to the affected entity types. fetch_metadata returns the value of the latest entry for each key (we use it while building the response models) and fetch_metadata_collection returns a list for each key containing entries with a value and a timestamp (we can potentially use this in the future to create histograms, this is just the initial version).

I also updated all the docs pages accordingly.

@schustmi Would be nice if you could read the changes I described above before diving into the PR.

[github-actions[bot]]

E2E template updates in examples/e2e have been pushed.

[bcdurak]

@avishniakov I also implemented some new changes, I thought you might like. You can check them in this commit.

Simply put, the log_metadata function now features two new parameters called infer_artifact and infer_model. So you can call log_metadata(metadata={}, infer_model=True) which will use the step_context and attach the metadata to the model. Thanks to this, I was also able to adjust the old functions like log_artifact_metadata to use the new log_metadata.

[github-actions[bot]]

LLM Finetuning template updates in examples/llm_finetuning have been pushed.

[github-actions[bot]]

Classification template updates in examples/mlops_starter have been pushed.

[github-actions[bot]]

E2E template updates in examples/e2e have been pushed.

[github-actions[bot]]

NLP template updates in examples/e2e_nlp have been pushed.